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forked from tdwojak/Python2018

laboratoria4

This commit is contained in:
s411752 2018-06-06 16:41:25 +02:00
parent 2ea7ef9e52
commit 4d03009ff6

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@ -1,14 +1,22 @@
#!/usr/bin/env python #!/usr/bin/env python
# -*- coding: utf-8 -*- # -*- coding: utf-8 -*-
import pandas as pd
from matplotlib import pyplot as plt
from sklearn import linear_model
def wczytaj_dane(): def wczytaj_dane():
pass data = pd.read_csv("J:/PycharmProjects/Python2018/labs06/mieszkania.csv")
return pd.DataFrame(data)
def most_common_room_number(dane): def most_common_room_number(dane):
pass dane2 = dane['Rooms'].value_counts().head(1)
pokoje = int(dane2.index[0])
return pokoje
def cheapest_flats(dane, n): def cheapest_flats(dane, n):
pass result = dane.sort_values('Expected').head(n)
return result
def find_borough(desc): def find_borough(desc):
dzielnice = ['Stare Miasto', dzielnice = ['Stare Miasto',
@ -19,23 +27,55 @@ def find_borough(desc):
'Winogrady', 'Winogrady',
'Miłostowo', 'Miłostowo',
'Dębiec'] 'Dębiec']
pass for i in range(0,len(dzielnice)):
if dzielnice[i] in desc:
result = dzielnice[i]
break
else:
result = 'Inne'
return result
def add_borough(dane): def add_borough(dane):
pass dane['Borough'] = dane['Location'].apply(find_borough)
return dane
def write_plot(dane, filename): def write_plot(dane, filename):
pass dane['Borough'].value_counts().plot(kind='barh')
plt.savefig('J:/PycharmProjects/Python2018/labs06/'+filename)
return 0
def mean_price(dane, room_number): def mean_price(dane, room_number):
pass dane2 = dane[dane.Rooms == room_number]
srednia = round(dane2.Expected.mean(),2)
return srednia
def find_13(dane): def find_13(dane):
pass dane2 = dane[dane.Floor == 13]
lista_dzielnic = dane2['Borough'].unique()
return lista_dzielnic
def find_best_flats(dane): def find_best_flats(dane):
pass dane2 = dane[(dane['Borough']=='Winogrady') & (dane['Rooms']==3) & (dane['Floor']==1)]
return dane2
def reg_lin(dane, metraz, pokoje):
reg = linear_model.LinearRegression()
reg.fit(dane[['SqrMeters', 'Rooms']], dane['Expected'])
result = reg.predict(pd.DataFrame([(metraz, pokoje)], columns=['var1', 'var2']))
return result
"""
dane = wczytaj_dane()
print(most_common_room_number(dane))
print(cheapest_flats(dane, 2))
print(find_borough('Winogrady i Jeżyce'))
add_borough(dane)
write_plot(dane, 'wykres')
print(mean_price(dane, 3))
print(find_13(dane))
print(find_best_flats(dane).shape[0])
print(reg_lin(dane, 60, 3))
"""
def main(): def main():
dane = wczytaj_dane() dane = wczytaj_dane()
@ -45,7 +85,7 @@ def main():
.format(most_common_room_number(dane))) .format(most_common_room_number(dane)))
print("{} to najłądniejsza dzielnica w Poznaniu." print("{} to najłądniejsza dzielnica w Poznaniu."
.format(find_borough("Grunwald i Jeżyce")))) .format(find_borough("Grunwald i Jeżyce")))
print("Średnia cena mieszkania 3-pokojowego, to: {}" print("Średnia cena mieszkania 3-pokojowego, to: {}"
.format(mean_price(dane, 3))) .format(mean_price(dane, 3)))